These conclusions are apparent from Figure REF . Here, we plot the COCO Val mAPs of the detection architectures against the ImageNet Val top-1 accuracies of their respective backbones.
First, we see that OFA models trained from scratch (OFA Scratch and OFA224) and models found in the similar MobileNetV3 (\(1.2\times \) ) search space through DONNA, transfer very similarly to COCO. Models found in the DONNA search space reach higher COCO mAP than expected based on their ImageNet top-1 accuracy. We suspect that such bias occurs because instead of strictly relying on depthwise convolutions, which is the case for MobileNetV3 (1.2\(\times \) ) space, grouped convolutions are used in the DONNA search space.
Second, we find that while OFA models with OFA training obtain around 1.0-1.5 percent higher accuracy on ImageNet [1]} than the same models trained from scratch, this increased accuracy does not transfer to a meaningful gain in downstream tasks such as object detection. This phenomenon is illustrated in Figure REF , where the same OFA models are trained on MS-COCO, either starting from weights trained on ImageNet from scratch or starting from weights obtained through progressive shrinking on ImageNet. For one of these models, the \(1.4\%\)  gain in ImageNet validation accuracy only translates into \(0.1\%\)  higher mAP on COCO. This observation motivates the choice that throughout the text, we compare to OFA-models which are trained from scratch rather than through progressive shrinking.
